




'''simple docstring'''


import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


UpperCAmelCase_					=   1e-4

if is_torch_available():
  import torch

  from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
  from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder





@require_torch
class  __lowercase    :







   def __init__(						self   ,				UpperCamelCase   ,				UpperCamelCase=16   ,				UpperCamelCase=13   ,				UpperCamelCase=7   ,				UpperCamelCase=14   ,				UpperCamelCase=10   ,				UpperCamelCase=19   ,				UpperCamelCase=5   ,				UpperCamelCase=4   ,				UpperCamelCase=True   ,				UpperCamelCase=16   ,				UpperCamelCase=2   ,				UpperCamelCase=4   ,				UpperCamelCase=4   ,				UpperCamelCase="gelu"   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.1   ,				UpperCamelCase=[1, 2, 3, 4, 5]   ,				UpperCamelCase=25   ,				UpperCamelCase=5   ,				)					->     Tuple:
     __a								=       d_model
     __a								=       parent
     __a								=       batch_size
     __a								=       prediction_length
     __a								=       context_length
     __a								=       cardinality
     __a								=       num_time_features
     __a								=       lags_sequence
     __a								=       embedding_dimension
     __a								=       is_training
     __a								=       hidden_size
     __a								=       num_hidden_layers
     __a								=       num_attention_heads
     __a								=       intermediate_size
     __a								=       hidden_act
     __a								=       hidden_dropout_prob
     __a								=       attention_probs_dropout_prob

     __a								=       context_length
     __a								=       prediction_length + label_length
     __a								=       label_length

     __a								=       moving_average
     __a								=       autocorrelation_factor







   def 		UpperCamelCase__      (						self   )					->     List[Any]:
     return AutoformerConfig(
         d_model=self.d_model   ,				encoder_layers=self.num_hidden_layers   ,				decoder_layers=self.num_hidden_layers   ,				encoder_attention_heads=self.num_attention_heads   ,				decoder_attention_heads=self.num_attention_heads   ,				encoder_ffn_dim=self.intermediate_size   ,				decoder_ffn_dim=self.intermediate_size   ,				dropout=self.hidden_dropout_prob   ,				attention_dropout=self.attention_probs_dropout_prob   ,				prediction_length=self.prediction_length   ,				context_length=self.context_length   ,				label_length=self.label_length   ,				lags_sequence=self.lags_sequence   ,				num_time_features=self.num_time_features   ,				num_static_categorical_features=1   ,				cardinality=[self.cardinality]   ,				embedding_dimension=[self.embedding_dimension]   ,				moving_average=self.moving_average   ,				)







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     List[Any]:
     __a								=       config.context_length + max(config.lags_sequence   )

     __a								=       ids_tensor([self.batch_size, 1]   ,				config.cardinality[0]   )
     __a								=       floats_tensor([self.batch_size, _past_length, config.num_time_features]   )
     __a								=       floats_tensor([self.batch_size, _past_length]   )
     __a								=       floats_tensor([self.batch_size, _past_length]   ) > 0.5

     # decoder inputs
     __a								=       floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]   )
     __a								=       floats_tensor([self.batch_size, config.prediction_length]   )

     __a								=       {
         'past_values': past_values,
         'static_categorical_features': static_categorical_features,
         'past_time_features': past_time_features,
         'past_observed_mask': past_observed_mask,
         'future_time_features': future_time_features,
         'future_values': future_values,
     }
     return inputs_dict







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     __a								=       self.get_config()
     __a								=       self.prepare_autoformer_inputs_dict(UpperCamelCase   )
     return config, inputs_dict







   def 		UpperCamelCase__      (						self   )					->     Tuple:
     __a						,							__a								=       self.prepare_config_and_inputs()
     return config, inputs_dict







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   )					->     Dict:
     __a								=       AutoformerModel(config=UpperCamelCase   ).to(UpperCamelCase   ).eval()
     __a								=       model(**UpperCamelCase   )

     __a								=       outputs.encoder_last_hidden_state
     __a								=       outputs.last_hidden_state

     with tempfile.TemporaryDirectory() as tmpdirname:
       __a								=       model.get_encoder()
       encoder.save_pretrained(UpperCamelCase   )
       __a								=       AutoformerEncoder.from_pretrained(UpperCamelCase   ).to(UpperCamelCase   )

     __a						,							__a						,							__a						,							__a						,							__a								=       model.create_network_inputs(**UpperCamelCase   )
     __a						,							__a								=       model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]   )

     __a								=       torch.cat(
         (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])   ,				dim=-1   ,				)
     __a								=       encoder(inputs_embeds=UpperCamelCase   )[0]
     self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3   )

     __a								=       (
         torch.mean(transformer_inputs[:, : config.context_length, ...]   ,				dim=1   )
         .unsqueeze(1   )
         .repeat(1   ,				config.prediction_length   ,				1   )
     )
     __a								=       torch.zeros(
         [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]]   ,				device=enc_input.device   ,				)

     __a								=       torch.cat(
         (
             torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)   ,				dim=1   ),
             feature[:, config.context_length - config.label_length :, ...],
         )   ,				dim=-1   ,				)
     __a								=       torch.cat(
         (
             torch.cat((trend_input[:, -config.label_length :, ...], mean)   ,				dim=1   ),
             feature[:, config.context_length - config.label_length :, ...],
         )   ,				dim=-1   ,				)

     with tempfile.TemporaryDirectory() as tmpdirname:
       __a								=       model.get_decoder()
       decoder.save_pretrained(UpperCamelCase   )
       __a								=       AutoformerDecoder.from_pretrained(UpperCamelCase   ).to(UpperCamelCase   )

     __a								=       decoder(
         trend=UpperCamelCase   ,				inputs_embeds=UpperCamelCase   ,				encoder_hidden_states=UpperCamelCase   ,				)[0]

     self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3   )





@require_torch
class  __lowercase    (	__magic_name__							,				__magic_name__							,				unittest.TestCase			):
   _a    						=   (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
   _a    						=   (AutoformerForPrediction,) if is_torch_available() else ()
   _a    						=   {"""feature-extraction""": AutoformerModel} if is_torch_available() else {}
   _a    						=   False
   _a    						=   False
   _a    						=   False
   _a    						=   False
   _a    						=   False
   _a    						=   False







   def 		UpperCamelCase__      (						self   )					->     List[Any]:
     __a								=       AutoformerModelTester(self   )
     __a								=       ConfigTester(self   ,				config_class=UpperCamelCase   ,				has_text_modality=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     self.config_tester.run_common_tests()







   def 		UpperCamelCase__      (						self   )					->     int:
     __a						,							__a								=       self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
       __a								=       model_class(UpperCamelCase   )

       with tempfile.TemporaryDirectory() as tmpdirname:
         model.save_pretrained(UpperCamelCase   )
         __a						,							__a								=       model_class.from_pretrained(UpperCamelCase   ,				output_loading_info=UpperCamelCase   )
       self.assertEqual(info['missing_keys']   ,				[]   )







   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_encoder_decoder_model_standalone(*UpperCamelCase   )







   @unittest.skip(reason='Model has no tokens embeddings'   )
   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     pass







   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     __a								=       inspect.signature(getattr(UpperCamelCase   ,				'forward'   )   )
     # The main input is the name of the argument after `self`
     __a								=       list(model_signature.parameters.keys()   )[1]
     self.assertEqual(AutoformerModel.main_input_name   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     __a						,							__a								=       self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       __a								=       model_class(UpperCamelCase   )
       __a								=       inspect.signature(model.forward   )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       __a								=       [*signature.parameters.keys()]

       __a								=       [
           'past_values',
           'past_time_features',
           'past_observed_mask',
           'static_categorical_features',
           'static_real_features',
           'future_values',
           'future_time_features',
       ]

       if model.__class__.__name__ in ["AutoformerForPrediction"]:
         expected_arg_names.append('future_observed_mask'   )

       expected_arg_names.extend(
           [
               'decoder_attention_mask',
               'head_mask',
               'decoder_head_mask',
               'cross_attn_head_mask',
               'encoder_outputs',
               'past_key_values',
               'output_hidden_states',
               'output_attentions',
               'use_cache',
               'return_dict',
           ]   )

       self.assertListEqual(arg_names[: len(UpperCamelCase   )]   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Tuple:
     __a						,							__a								=       self.model_tester.prepare_config_and_inputs_for_common()
     __a								=       True

     __a								=       getattr(self.model_tester   ,				'seq_length'   ,				UpperCamelCase   )
     __a								=       getattr(self.model_tester   ,				'decoder_seq_length'   ,				UpperCamelCase   )
     __a								=       getattr(self.model_tester   ,				'encoder_seq_length'   ,				UpperCamelCase   )
     __a								=       getattr(self.model_tester   ,				'd_model'   ,				UpperCamelCase   )
     __a								=       getattr(self.model_tester   ,				'num_attention_heads'   ,				UpperCamelCase   )
     __a								=       d_model // num_attention_heads

     for model_class in self.all_model_classes:
       __a								=       True
       __a								=       False
       __a								=       True
       __a								=       model_class(UpperCamelCase   )
       model.to(UpperCamelCase   )
       model.eval()
       with torch.no_grad():
         __a								=       model(**self._prepare_for_class(UpperCamelCase   ,				UpperCamelCase   )   )
       __a								=       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
       self.assertEqual(len(UpperCamelCase   )   ,				self.model_tester.num_hidden_layers   )

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       __a								=       True
       __a								=       model_class(UpperCamelCase   )
       model.to(UpperCamelCase   )
       model.eval()
       with torch.no_grad():
         __a								=       model(**self._prepare_for_class(UpperCamelCase   ,				UpperCamelCase   )   )
       __a								=       outputs.encoder_attentions
       self.assertEqual(len(UpperCamelCase   )   ,				self.model_tester.num_hidden_layers   )

       self.assertListEqual(
           list(attentions[0].shape[-3:]   )   ,				[self.model_tester.num_attention_heads, encoder_seq_length, dim]   ,				)
       __a								=       len(UpperCamelCase   )

       __a								=       7

       if "last_hidden_state" in outputs:
         correct_outlen += 1

       if "trend" in outputs:
         correct_outlen += 1

       if "past_key_values" in outputs:
         correct_outlen += 1  # past_key_values have been returned

       if "loss" in outputs:
         correct_outlen += 1

       if "params" in outputs:
         correct_outlen += 1

       self.assertEqual(UpperCamelCase   ,				UpperCamelCase   )

       # decoder attentions
       __a								=       outputs.decoder_attentions
       self.assertIsInstance(UpperCamelCase   ,				(list, tuple)   )
       self.assertEqual(len(UpperCamelCase   )   ,				self.model_tester.num_hidden_layers   )
       self.assertListEqual(
           list(decoder_attentions[0].shape[-3:]   )   ,				[self.model_tester.num_attention_heads, decoder_seq_length, dim]   ,				)

       # cross attentions
       __a								=       outputs.cross_attentions
       self.assertIsInstance(UpperCamelCase   ,				(list, tuple)   )
       self.assertEqual(len(UpperCamelCase   )   ,				self.model_tester.num_hidden_layers   )
       self.assertListEqual(
           list(cross_attentions[0].shape[-3:]   )   ,				[self.model_tester.num_attention_heads, decoder_seq_length, dim]   ,				)

     # Check attention is always last and order is fine
     __a								=       True
     __a								=       True
     __a								=       model_class(UpperCamelCase   )
     model.to(UpperCamelCase   )
     model.eval()
     with torch.no_grad():
       __a								=       model(**self._prepare_for_class(UpperCamelCase   ,				UpperCamelCase   )   )

     self.assertEqual(out_len + 2   ,				len(UpperCamelCase   )   )

     __a								=       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

     self.assertEqual(len(UpperCamelCase   )   ,				self.model_tester.num_hidden_layers   )
     self.assertListEqual(
         list(self_attentions[0].shape[-3:]   )   ,				[self.model_tester.num_attention_heads, encoder_seq_length, dim]   ,				)







   @is_flaky()
   def 		UpperCamelCase__      (						self   )					->     str:
     super().test_retain_grad_hidden_states_attentions()


def     SCREAMING_SNAKE_CASE							(   a_						:				List[str]="train-batch.pt"      ):
  __a								=       hf_hub_download(repo_id='hf-internal-testing/tourism-monthly-batch'					,       filename=a_					,       repo_type='dataset'      )
  __a								=       torch.load(a_					,       map_location=a_      )
  return batch





@require_torch
@slow
class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     __a								=       AutoformerModel.from_pretrained('huggingface/autoformer-tourism-monthly'   ).to(UpperCamelCase   )
     __a								=       prepare_batch()

     with torch.no_grad():
       __a								=       model(
           past_values=batch['past_values']   ,				past_time_features=batch['past_time_features']   ,				past_observed_mask=batch['past_observed_mask']   ,				static_categorical_features=batch['static_categorical_features']   ,				future_values=batch['future_values']   ,				future_time_features=batch['future_time_features']   ,				)[0]

     __a								=       torch.Size(
         (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)   )
     self.assertEqual(output.shape   ,				UpperCamelCase   )

     __a								=       torch.tensor(
         [[0.3_593, -1.3_398, 0.6_330], [0.2_279, 1.5_396, -0.1_792], [0.0_450, 1.3_225, -0.2_335]]   ,				device=UpperCamelCase   )
     self.assertTrue(torch.allclose(output[0, :3, :3]   ,				UpperCamelCase   ,				atol=UpperCamelCase   )   )







   def 		UpperCamelCase__      (						self   )					->     Dict:
     __a								=       AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'   ).to(UpperCamelCase   )
     __a								=       prepare_batch('val-batch.pt'   )
     with torch.no_grad():
       __a								=       model(
           past_values=batch['past_values']   ,				past_time_features=batch['past_time_features']   ,				past_observed_mask=batch['past_observed_mask']   ,				static_categorical_features=batch['static_categorical_features']   ,				).encoder_last_hidden_state
     __a								=       torch.Size((64, model.config.context_length, model.config.d_model)   )
     self.assertEqual(output.shape   ,				UpperCamelCase   )

     __a								=       torch.tensor(
         [[-0.0_734, -0.9_036, 0.8_358], [4.7_186, 2.4_113, 1.9_581], [1.7_953, 2.3_558, 1.2_970]]   ,				device=UpperCamelCase   )
     self.assertTrue(torch.allclose(output[0, :3, :3]   ,				UpperCamelCase   ,				atol=UpperCamelCase   )   )







   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'   ).to(UpperCamelCase   )
     __a								=       prepare_batch('val-batch.pt'   )
     with torch.no_grad():
       __a								=       model.generate(
           static_categorical_features=batch['static_categorical_features']   ,				past_time_features=batch['past_time_features']   ,				past_values=batch['past_values']   ,				future_time_features=batch['future_time_features']   ,				past_observed_mask=batch['past_observed_mask']   ,				)
     __a								=       torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)   )
     self.assertEqual(outputs.sequences.shape   ,				UpperCamelCase   )

     __a								=       torch.tensor([3_130.6_763, 4_056.5_293, 7_053.0_786]   ,				device=UpperCamelCase   )
     __a								=       outputs.sequences.mean(dim=1   )
     self.assertTrue(torch.allclose(mean_prediction[0, -3:]   ,				UpperCamelCase   ,				rtol=1e-1   )   )









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_					=   {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   ["FNetTokenizer"]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   ["FNetTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "FNetForMaskedLM",
      "FNetForMultipleChoice",
      "FNetForNextSentencePrediction",
      "FNetForPreTraining",
      "FNetForQuestionAnswering",
      "FNetForSequenceClassification",
      "FNetForTokenClassification",
      "FNetLayer",
      "FNetModel",
      "FNetPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_fnet import FNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_fnet_fast import FNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_fnet import (
        FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        FNetForMaskedLM,
        FNetForMultipleChoice,
        FNetForNextSentencePrediction,
        FNetForPreTraining,
        FNetForQuestionAnswering,
        FNetForSequenceClassification,
        FNetForTokenClassification,
        FNetLayer,
        FNetModel,
        FNetPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



